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Atomic Nb-doping of WS₂ for high-performance synaptic transistors in neuromorphic computing

Engineering and Technology

Atomic Nb-doping of WS₂ for high-performance synaptic transistors in neuromorphic computing

K. Guan, Y. Li, et al.

Discover how researchers Kejie Guan, Yinxiao Li, Lin Liu, Fuqin Sun, Yingyi Wang, Zhuo Zheng, Weifan Zhou, Cheng Zhang, Zhengyang Cai, Xiaowei Wang, Simin Feng, and Ting Zhang transformed WS₂ into a powerful synaptic transistor for neuromorphic computing by doping it with niobium, significantly enhancing its switch ratio and achieving impressive recognition accuracy on the MNIST dataset.

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~3 min • Beginner • English
Abstract
Owing to the controllable growth and large-area synthesis for high-density integration, interest in employing atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDCs) for synaptic transistors is increasing. In particular, substitutional doping of 2D materials allows flexible modulation of material physical properties, facilitating precise control in defect engineering for eventual synaptic plasticity. In this study, to increase the switch ratio of synaptic transistors, we selectively performed experiments on WS₂ and introduced niobium (Nb) atoms to serve as the channel material. The Nb atoms were substitutionally doped at the W sites, forming a uniform distribution across the entire flakes. The synaptic transistor devices exhibited an improved switch ratio of 10³, 100 times larger than that of devices prepared with undoped WS₂. The Nb atoms in WS₂ play crucial roles in trapping and detrapping electrons. The modulation of channel conductivity achieved through the gate effectively simulates synaptic potentiation, inhibition, and repetitive learning processes. The Nb-WS₂ synaptic transistor achieves 92.30% recognition accuracy on the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset after 125 training iterations. This study's contribution extends to a pragmatic and accessible atomic doping methodology, elucidating the strategies underlying doping techniques for channel materials in synaptic transistors.
Publisher
Microsystems & Nanoengineering
Published On
Jan 01, 2024
Authors
Kejie Guan, Yinxiao Li, Lin Liu, Fuqin Sun, Yingyi Wang, Zhuo Zheng, Weifan Zhou, Cheng Zhang, Zhengyang Cai, Xiaowei Wang, Simin Feng, Ting Zhang
Tags
2D materials
transition-metal dichalcogenides
neuromorphic computing
synaptic transistors
dopants
WS₂
machine learning
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